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Cited by 263Radiomics and Machine Learning in Medical ImagingAI in cancer detectionLung Cancer Diagnosis and Treatment

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Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma

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Grove, Olya, Berglund, Anders E., Schabath, Matthew B., Aerts, Hugo J. W. L., Dekker, Andre, Wang, Hua, Velazquez, Emmanuel Rios, Lambin, Philippe, Gu, Yuhua, Balagurunathan, Yoganand, Eikman, Edward, Gatenby, Robert A., Eschrich, Steven, Gillies, Robert J.·2015·PLOS ONE
DOI 10.1371/journal.pone.0118261PMC PMC4349806
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Abstract

Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.

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Crossref · 10.1371/journal.pone.0118261

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Figures

Fig 1 Convexity feature was developed to quantify tumor shape. Convexity is computed as a ratio of tumor border (blue) to convex hull (red) (a). Convexity feature tracks the change in tumor morphology (b). Convexity is predictive of patient overall survival when dichotomized at the median value (c).
Fig 1 Convexity feature was developed to quantify tumor shape. Convexity is computed as a ratio of tumor border (blue) to convex hull (red) (a). Convexity feature tracks the change in tumor morphology (b). Convexity is predictive of patient overall survival when dichotomized at the median value (c).CC BY · PubMed Central
Fig 2 Entropy ratio was developed to quantify intensity variations across the tumor. While some tumors present with consistent mean entropy across the core and the boundary (a), others have a distinct difference in the values (b).
Fig 2 Entropy ratio was developed to quantify intensity variations across the tumor. While some tumors present with consistent mean entropy across the core and the boundary (a), others have a distinct difference in the values (b).CC BY · PubMed Central
Fig 3 Entropy ratio between the core and border regions of the tumor is predictive of patient survival. The tumors in the two prognostic groups (a) did not appear significantly different in the CT scans (b).
Fig 3 Entropy ratio between the core and border regions of the tumor is predictive of patient survival. The tumors in the two prognostic groups (a) did not appear significantly different in the CT scans (b).CC BY · PubMed Central
Fig 4 Histogram of the two imaging features across cohorts. Convexity (a) shows similar range across cohorts (training-green, test-blue) However, training cohort is enriched with round tumors. The range of values for entropy ratio feature (b) is larger in training cohort. Both convexity (a) and entropy ratio (b) consistently capture targeted tumor characteristics in both cohorts.
Fig 4 Histogram of the two imaging features across cohorts. Convexity (a) shows similar range across cohorts (training-green, test-blue) However, training cohort is enriched with round tumors. The range of values for entropy ratio feature (b) is larger in training cohort. Both convexity (a) and entropy ratio (b) consistently capture targeted tumor characteristics in both cohorts.CC BY · PubMed Central

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Metadata

Authors
Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., Eikman, E., Gatenby, R. A., Eschrich, S., & Gillies, R. J.
Affiliations
  • Moffitt Cancer Center (US)
  • Brigham and Women's Hospital (US)
  • Maastricht University (NL)
  • Tianjin Medical University Cancer Institute and Hospital (CN)
Corresponding author
Gillies, Robert J.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-03-04
Volume
10
Issue
3
Pages
e0118261
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0118261
PMC
PMC4349806
OpenAlex
W2067959276
Cited by
263
References
30
Citations by year
2026: 9 · 2025: 12 · 2024: 16 · 2023: 19 · 2022: 18 · 2021: 32 · 2020: 38 · 2019: 32 · 2018: 34 · 2017: 27 · 2016: 22 · 2015: 4
FWCI
23.50
Type
journal article
Language
en
Update notices
correction (2021-03-08)
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion, cc-by)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Research Publications (Maastricht University) (submittedVersion, other-oa)
Funders
  • National Institutes of Health (NIH/NCI P50CA119997)
  • National Cancer Institute (U01CA143062, P50CA119997, U01CA196405, P30CA076292, R25CA174664)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
Keywords
lung adenocarcinoma, intratumoral heterogeneity, CT imaging features, prognostic factors, overall survival, tumor segmentation, reproducibility
MeSH terms
Adenocarcinoma of Lung, Adenocarcinoma, Aged, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Male, Middle Aged, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Proportional Hazards Models, Entropy

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